The dynamics of the skeletal muscle: A systems biophysics perspective on muscle modeling with the focus on Hill‐type muscle models

Schmitt, Syn; Günther, Michael; Häufle, Daniel

doi:10.1002/gamm.201900013

Cited by 17 publications

(13 citation statements)

References 155 publications

(186 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Individually, these criteria have been fulfilled in models before. For criterion (a), models typically consider muscle fiber characteristics (Hill-type muscle models, e.g., Millard et al, 2013;Haeufle et al, 2014b;Siebert and Rode, 2014), tendon non-linear elasticity, neuro-muscular activation dynamics (e.g., Hatze, 1977;Rockenfeller et al, 2015), antagonistic setup (e.g., Schmitt et al, 2019), and anatomical muscle routing (e.g., Holzbaur et al, 2005;Hammer et al, 2019). Such models are used for ergonomics or for the development of assistive devices, but, to our knowledge, do not fulfill at least one of the other two criteria (Holzbaur et al, 2005;Chadwick et al, 2009;Loeb, 2012;Glenday et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Predicting Perturbed Human Arm Movements in a Neuro-Musculoskeletal Model to Investigate the Muscular Force Response

Stollenmaier

Ilg

Haeufle

2020

Front. Bioeng. Biotechnol.

View full text Add to dashboard Cite

Human movement is generated by a dynamic interplay between the nervous system, the biomechanical structures, and the environment. To investigate this interaction, we propose a neuro-musculoskeletal model of human goal-directed arm movements. Using this model, we simulated static perturbations of the inertia and damping properties of the arm, as well as dynamic torque perturbations for one-degree-of freedom movements around the elbow joint. The controller consists of a feed-forward motor command and feedback based on muscle fiber length and contraction velocity representing short-latency (25 ms) or long-latency (50 ms) stretch reflexes as the first neuronal responses elicited by an external perturbation. To determine the open-loop control signal, we parameterized the control signal resulting in a piecewise constant stimulation over time for each muscle. Interestingly, such an intermittent open-loop signal results in a smooth movement that is close to experimental observations. So, our model can generate the unperturbed point-to-point movement solely by the feed-forward command. The feedback only contributed to the stimulation in perturbed movements. We found that the relative contribution of this feedback is small compared to the feed-forward control and that the characteristics of the musculoskeletal system create an immediate and beneficial reaction to the investigated perturbations. The novelty of these findings is (1) the reproduction of static as well as dynamic perturbation experiments in one neuro-musculoskeletal model with only one set of basic parameters. This allows to investigate the model's neuro-muscular response to the perturbations that-at least to some degree-represent stereotypical interactions with the environment; (2) the demonstration that in feed-forward driven movements the muscle characteristics generate a mechanical response with zero-time delay which helps to compensate for the perturbations; (3) that this model provides enough biomechanical detail to allow for the prediction of internal forces, including joint loads and muscle-bone contact forces which are relevant in ergonomics and for the development of assistive devices but cannot be observed in experiments.

show abstract

Section: Introductionmentioning

confidence: 99%

Predicting Perturbed Human Arm Movements in a Neuro-Musculoskeletal Model to Investigate the Muscular Force Response

Stollenmaier

Ilg

Haeufle

2020

Front. Bioeng. Biotechnol.

View full text Add to dashboard Cite

show abstract

“…Both studies do not take into account the interaction of the individual, non-linear muscle dynamics with the non-linear dynamics of the skeleton. However, it is known that muscles with their characteristic activation dynamics, non-linearities, elasticities, and antagonistic setup contribute to the characteristics of biological movement (van Soest and Bobbert, 1993;Daley et al, 2009;Schmitt et al, 2019) which has consequences for the interpretation of the underlying motor control principles (Pinter et al, 2012). Thus, the question is whether individual muscle dynamics play a significant role in the optimality of motion generation and control for point-to-manifold tasks?…”

Section: Introductionmentioning

confidence: 99%

Optimality Principles in Human Point-to-Manifold Reaching Accounting for Muscle Dynamics

Wochner

Driess

Zimmermann

et al. 2020

Front. Comput. Neurosci.

Self Cite

View full text Add to dashboard Cite

Human arm movements are highly stereotypical under a large variety of experimental conditions. This is striking due to the high redundancy of the human musculoskeletal system, which in principle allows many possible trajectories toward a goal. Many researchers hypothesize that through evolution, learning, and adaption, the human system has developed optimal control strategies to select between these possibilities. Various optimality principles were proposed in the literature that reproduce human-like trajectories in certain conditions. However, these studies often focus on a single cost function and use simple torque-driven models of motion generation, which are not consistent with human muscle-actuated motion. The underlying structure of our human system, with the use of muscle dynamics in interaction with the control principles, might have a significant influence on what optimality principles best model human motion. To investigate this hypothesis, we consider a point-to-manifold reaching task that leaves the target underdetermined. Given hypothesized motion objectives, the control input is generated using Bayesian optimization, which is a machine learning based method that trades-off exploitation and exploration. Using numerical simulations with Hill-type muscles, we show that a combination of optimality principles best predicts human point-to-manifold reaching when accounting for the muscle dynamics.

show abstract

“…The muscles are lumped (mono-articular) constructs, implementing the concept of an ‘elementary biological drive’ (Schmitt et al. 2019 ) for joint actuation: for each hinge joint, there is exactly one flexor (Fx) and one extensor (Ex) muscle.…”

Section: Model and Control Frameworkmentioning

confidence: 99%

A geometry- and muscle-based control architecture for synthesising biological movement

et al. 2021

Self Cite

View full text Add to dashboard Cite

A key problem for biological motor control is to establish a link between an idea of a movement and the generation of a set of muscle-stimulating signals that lead to the movement execution. The number of signals to generate is thereby larger than the body’s mechanical degrees of freedom in which the idea of the movement may be easily expressed, as the movement is actually executed in this space. A mathematical formulation that provides a solving link is presented in this paper in the form of a layered, hierarchical control architecture. It is meant to synthesise a wide range of complex three-dimensional muscle-driven movements. The control architecture consists of a ‘conceptional layer’, where the movement is planned, a ‘structural layer’, where the muscles are stimulated, and between both an additional ‘transformational layer’, where the muscle-joint redundancy is resolved. We demonstrate the operativeness by simulating human stance and squatting in a three-dimensional digital human model (DHM). The DHM considers 20 angular DoFs and 36 Hill-type muscle–tendon units (MTUs) and is exposed to gravity, while its feet contact the ground via reversible stick–slip interactions. The control architecture continuously stimulates all MTUs (‘structural layer’) based on a high-level, torque-based task formulation within its ‘conceptional layer’. Desired states of joint angles (postural plan) are fed to two mid-level joint controllers in the ‘transformational layer’. The ‘transformational layer’ communicates with the biophysical structures in the ‘structural layer’ by providing direct MTU stimulation contributions and further input signals for low-level MTU controllers. Thereby, the redundancy of the MTU stimulations with respect to the joint angles is resolved, i.e. a link between plan and execution is established, by exploiting some properties of the biophysical structures modelled. The resulting joint torques generated by the MTUs via their moment arms are fed back to the conceptional layer, closing the high-level control loop. Within our mathematical formulations of the Jacobian matrix-based layer transformations, we identify the crucial information for the redundancy solution to be the muscle moment arms, the stiffness relations of muscle and tendon tissue within the muscle model, and the length–stimulation relation of the muscle activation dynamics. The present control architecture allows the straightforward feeding of conceptional movement task formulations to MTUs. With this approach, the problem of movement planning is eased, as solely the mechanical system has to be considered in the conceptional plan.

show abstract

The dynamics of the skeletal muscle: A systems biophysics perspective on muscle modeling with the focus on Hill‐type muscle models

Cited by 17 publications

References 155 publications

Predicting Perturbed Human Arm Movements in a Neuro-Musculoskeletal Model to Investigate the Muscular Force Response

Predicting Perturbed Human Arm Movements in a Neuro-Musculoskeletal Model to Investigate the Muscular Force Response

Optimality Principles in Human Point-to-Manifold Reaching Accounting for Muscle Dynamics

A geometry- and muscle-based control architecture for synthesising biological movement

Contact Info

Product

Resources

About